1. Field of the Invention
The subject invention relates to an assembly for sensing moisture on a sheet of glass and, more particularly, to a rain sensor for detecting rain on the windshield of an automotive vehicle to turn on the wipers.
2. Description of the Prior Art
The present invention relates to a method of analyzing the information developed by an off the glass imaging rain sensor. Optical rain sensing means as disclosed in the co-pending application mentioned above may be used to generate a digital image of rain drops, mist, water rivulets and other artifacts that may appear on the outer surface of a vehicular windshield. In addition, these same imaging means may develop an `image` of fog that may condense on the inner surface of a windshield under certain conditions. As described in the co-pending application, an optical lens, or reflector, forms a real image of the rain on a focal plane imaging array. Such arrays are well known for their use in digital imaging and in solid state T.V. cameras. Currently, two of the more common imaging array technologies are charge coupled devices [CCD] or complementary metal oxide semiconductor [CMOS] devices.
Typically, in digital imaging practice, the scanned image is composed of sequential pixels or image elements that are stored as a coherent image in some form of memory. That is, each pixel's luminance level is stored as a digital value in a memory location corresponding to its location in the image array. Naturally, in monochrome applications chromaticity information is not pertinent. However, even without chromaticity information the amount of digital storage and manipulation necessary for even moderate resolution images is very large. For example, a low cost imager with 160.times.120 pixel resolution would require over 25 Kbyte storage for each image frame, when the overhead of blank lines is factored in. At video framing rates of 30 Hz. the parallel video data streams at over 750 Kbytes per second, necessitating high speed, relatively costly processing.
Traditional image processing algorithms which include two dimensional edge detection, convolution, correlation and frequency transform algorithms are extremely memory and computationally intensive. These techniques are primarily used to recognize specific objects within an image. Common applications for such image analysis methods include counting biological cells of various types and for determining the number of inclusions in metallographic samples. Such image analysis is particularly useful when the image characteristics of the subject are well established and can be quantified. In applications where unknown artifacts may poison the image, traditional image analysis methods are less useful, and provisions have to be made to process and store such extraneous data in expensive non-volatile memory. For example, in a typical rains sensor application, chips and scratches in the windshield as well as non removable dirt would need to be identified as permanent artifacts that do not require wiping.
While traditional image analysis is characterized by the ability to recognize objects within one scanned image frame, or two at most, the computationally intensive algorithms that are necessary tend to make this approach rather expensive. There is clearly a need for image analysis that does not require full frame image storage and a need to reduce the computational requirements to a level available in low cost microcontrollers.